Sensory prediction error drives subconscious motor learning outside of the laboratory

Sensorimotor adaptation is supported by at least two parallel learning systems: an intentionally controlled explicit strategy, and an involuntary implicit learning system. To investigate the error sources driving these two systems, past work focused on constrained reaches or finger movements in laboratory environments has shown subconscious learning systems to be driven in part by sensory prediction error (SPE), i.e., the mismatch between the realized and expected outcome of an action. We designed a ball rolling task to explore whether SPEs can drive implicit motor adaptation during complex whole-body movements that impart physical motion on external objects. After applying a visual shift, participants rapidly adapted their rolling angles to reduce the error between the ball and target. We removed all visual feedback and told participants to aim their throw directly toward the primary target, revealing an unintentional 5.06{degrees} implicit adjustment to reach angles that decayed over time. To determine whether this implicit adaptation was driven by SPE, we gave participants a second aiming target that would solve the visual shift, as in Mazzoni and Krakauer (2006). Remarkably, after rapidly reducing ball rolling error to zero (due to enhancements in strategic aiming), the additional aiming target caused rolling angles to deviate beyond the primary target by 3.15{degrees}. This involuntary overcompensation, which worsened task performance, is a hallmark of SPE-driven implicit learning. These results show that SPE-driven implicit processes, previously observed within simplified finger or planar reaching movements, actively contribute to motor adaptation in more complex naturalistic skill-based tasks. New and NoteworthyImplicit and explicit learning systems have been detected using simple, constrained movements inside the laboratory. How these systems impact movements during complex whole-body, skill-based tasks has not been established. Here we demonstrate that sensory prediction errors significantly impact how a person updates their movements, replicating findings from the laboratory in an unconstrained ball-rolling task. This real-world validation is an important step towards explaining how subconscious learning helps humans execute common motor skills in dynamic environments.